Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds

Hadwin, Paul J.; Galindo, Gabriel E.; Daun, Kyle J.; Zañartu, Matías; Erath, Byron D.; Cataldo, Edson; Peterson, Sean D.

doi:10.1121/1.4948755

Cited by 26 publications

(18 citation statements)

References 53 publications

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“…Whereas the majority of modeling efforts in speech, to date, have employed models with general population-based parameters to uncover the universal physical underpinnings of human phonation, a few research teams have begun to explore the development of subject-specific numerical models for phonation [9][10][11][12][13]. The dynamics of the VFs are sensitive to a variety of factors, including subglottal pressure [14], laryngeal muscle activation [15], and a posterior glottal gap [4], to name a few.…”

Section: Introductionmentioning

confidence: 99%

“…These factors, which can play a role in vocal hyperfunction [16] and other pathologies [17][18][19][20], can be a challenge to observe clinically. Subject-specific models, on the other hand, are constructed based on measurements of the subject through less challenging media, such as high speed videoendoscopy (HSV) [9,11,21] and offer the potential to elucidate clinically opaque features and parameters, such as VF contact pressures.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

et al. 2019

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Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3 % for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

et al. 2019

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“…Other researchers have used genetic algorithms to optimize model parameters to match recorded glottal area, trajectory, and glottal volume wave and have shown the possibility of model inversion [ 22 , 23 ]. Tao extracted the physiologically relevant parameters of the vocal fold model from high-speed video image series [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Pathological Voice Source Analysis System Using a Flow Waveform-Matched Biomechanical Model

Zhang

Wei

et al. 2018

Applied Bionics and Biomechanics

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Voice production occurs through vocal cord and vibration coupled to glottal airflow. Vocal cord lesions affect the vocal system and lead to voice disorders. In this paper, a pathological voice source analysis system is designed. This study integrates nonlinear dynamics with an optimized asymmetric two-mass model to explore nonlinear characteristics of vocal cord vibration, and changes in acoustic parameters, such as fundamental frequency, caused by distinct subglottal pressure and varying degrees of vocal cord paralysis are analyzed. Various samples of sustained vowel /a/ of normal and pathological voices were extracted from MEEI (Massachusetts Eye and Ear Infirmary) database. A fitting procedure combining genetic particle swarm optimization and a quasi-Newton method was developed to optimize the biomechanical model parameters and match the targeted voice source. Experimental results validate the applicability of the proposed model to reproduce vocal cord vibration with high accuracy, and show that paralyzed vocal cord increases the model coupling stiffness.

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“…As cost function the Euclidean distance of the glottal area waveform was chosen. Lately, statistical methods like a non-stationary Bayesian estimation approach were suggested for optimizing 2MMs but was only tested on theoretical (i.e., simulated) vocal fold oscillations [ 39 ]. Further, a time-dependent 2MM was successfully adapted for 20 healthy and pathological voices [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Biomechanical simulation of vocal fold dynamics in adults based on laryngeal high-speed videoendoscopy

et al. 2017

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MotivationHuman voice is generated in the larynx by the two oscillating vocal folds. Owing to the limited space and accessibility of the larynx, endoscopic investigation of the actual phonatory process in detail is challenging. Hence the biomechanics of the human phonatory process are still not yet fully understood. Therefore, we adapt a mathematical model of the vocal folds towards vocal fold oscillations to quantify gender and age related differences expressed by computed biomechanical model parameters.MethodsThe vocal fold dynamics are visualized by laryngeal high-speed videoendoscopy (4000 fps). A total of 33 healthy young subjects (16 females, 17 males) and 11 elderly subjects (5 females, 6 males) were recorded. A numerical two-mass model is adapted to the recorded vocal fold oscillations by varying model masses, stiffness and subglottal pressure. For adapting the model towards the recorded vocal fold dynamics, three different optimization algorithms (Nelder–Mead, Particle Swarm Optimization and Simulated Bee Colony) in combination with three cost functions were considered for applicability. Gender differences and age-related kinematic differences reflected by the model parameters were analyzed.Results and conclusionThe biomechanical model in combination with numerical optimization techniques allowed phonatory behavior to be simulated and laryngeal parameters involved to be quantified. All three optimization algorithms showed promising results. However, only one cost function seems to be suitable for this optimization task. The gained model parameters reflect the phonatory biomechanics for men and women well and show quantitative age- and gender-specific differences. The model parameters for younger females and males showed lower subglottal pressures, lower stiffness and higher masses than the corresponding elderly groups. Females exhibited higher subglottal pressures, smaller oscillation masses and larger stiffness than the corresponding similar aged male groups.Optimizing numerical models towards vocal fold oscillations is useful to identify underlying laryngeal components controlling the phonatory process.

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Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds

Cited by 26 publications

References 53 publications

Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

Pathological Voice Source Analysis System Using a Flow Waveform-Matched Biomechanical Model

Biomechanical simulation of vocal fold dynamics in adults based on laryngeal high-speed videoendoscopy

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